Biogeographical Patterns of Species Richness and Abundance Distribution in Stream Diatoms Are Driven by Climate and Water Chemistry.

In this intercontinental study of stream diatoms, we asked three important but still unresolved ecological questions: (1) What factors drive the biogeography of species richness and species abundance distribution (SAD)? (2) Are climate-related hypotheses, which have dominated the research on the latitudinal and altitudinal diversity gradients, adequate in explaining spatial biotic variability? and (3) Is the SAD response to the environment independent of richness? We tested a number of climatic theories and hypotheses (i.e., the species-energy theory, the metabolic theory, the energy variability hypothesis, and the climatic tolerance hypothesis) but found no support for any of these concepts, as the relationships of richness with explanatory variables were nonexistent, weak, or unexpected. Instead, we demonstrated that diatom richness and SAD evenness generally increased with temperature seasonality and at mid- to high total phosphorus concentrations. The spatial patterns of diatom richness and the SAD-mainly longitudinal in the United States but latitudinal in Finland-were defined primarily by the covariance of climate and water chemistry with space. The SAD was not entirely controlled by richness, emphasizing its utility for ecological research. Thus, we found support for the operation of both climate and water chemistry mechanisms in structuring diatom communities, which underscores their complex response to the environment and the necessity for novel predictive frameworks.

A new perspective on the spatial, environmental, and metacommunity controls of local biodiversity.

Influential ecological research in the 1980s, elucidating that local biodiversity (LB) is a function of local ecological factors and the size of the regional species pool (γ-diversity), has prompted numerous investigations on the local and regional origins of LB. These investigations, however, have been mostly limited to single scales and target groups and centered exclusively on γ-diversity. Here we developed a unified framework including scale, environmental factors (heterogeneity and ambient levels), and metacommunity properties (intraspecific spatial aggregation, regional evenness, and γ-diversity) as hierarchical predictors of LB. We tested this framework with variance partitioning and structural equation modeling using subcontinental data on stream diatoms, insects, and fish as well as local physicochemistry, climate, and land use. Pure aggregation + regional evenness outperformed pure γ-diversity in explaining LB across groups. The covariance of the environment with aggregation + regional evenness rather than with γ-diversity generally explained a much greater proportion of the variance in diatom and insect LB, especially at smaller scales. Thus, disregarding aggregation and regional evenness, as commonly done, may lead to gross underestimation of the pure metacommunity effects and the indirect environmental effects on LB. We examined the shape of the local-regional species richness relationship, which has been widely used to infer local vs. regional effects on LB. We showed that this shape has an ecological basis, but its interpretation is not straightforward. Therefore, we advocate that the variance partitioning analysis under the proposed framework is adopted instead. In diatoms, metacommunity properties had the greatest total effects on LB, while in insects and fish, it was the environment, suggesting that larger organisms are more strongly controlled by the environment. Broader use of our framework may lead to novel biogeographical insights into the drivers of LB and improved projections of its trends along current and future environmental gradients.

On the shape and origins of the freshwater species-area relationship.

The species-area relationship (SAR) has over a 150-year-long history in ecology, but how its shape and origins vary across scales and organisms remains incompletely understood. This is the first subcontinental freshwater study to examine both these properties of the SAR in a spatially explicit way across major organismal groups (diatoms, insects, and fish) that differ in body size and dispersal capacity. First, to describe the SAR shape, we evaluated the fit of three commonly used models, logarithmic, power, and Michaelis-Menten. Second, we proposed a hierarchical framework to explain the variability in the SAR shape, captured by the parameters of the SAR model. According to this framework, scale and species group were the top predictors of the SAR shape, climatic factors (heterogeneity and median conditions) represented the second predictor level, and metacommunity properties (intraspecific spatial aggregation, γ-diversity, and species abundance distribution) the third predictor level. We calculated the SAR as a sample-based rarefaction curve using 60 streams within landscape windows (scales) in the United States, ranging from 160,000 to 6,760,000 km2 . First, we found that all models provided good fits (R2 ≥ 0.93), but the frequency of the best-fitting model was strongly dependent on organism, scale, and metacommunity properties. The Michaelis-Menten model was most common in fish, at the largest scales, and at the highest levels of intraspecific spatial aggregation. The power model was most frequent in diatoms and insects, at smaller scales, and in metacommunities with the lowest evenness. The logarithmic model fit best exclusively at the smallest scales and in species-poor metacommunities, primarily fish. Second, we tested our framework with the parameters of the most broadly used SAR model, the log-log form of the power model, using a structural equation model. This model supported our framework and revealed that the SAR slope was best predicted by scale- and organism-dependent metacommunity properties, particularly spatial aggregation, whereas the intercept responded most strongly to species group and γ-diversity. Future research should investigate from the perspective of our framework how shifts in metacommunity properties due to climate change may alter the SAR.